Mandate failure rarely announces itself. It accumulates quietly — a slight drop in response rates, a recruiter who is slightly overloaded, a hiring manager who takes a day longer to give feedback. None of these events triggers an alarm. Together, they predict a stalled search with high accuracy.
The Failure Prediction Engine is the layer of Majhi OS that detects these accumulating patterns before they become a crisis. It doesn't wait for a mandate to fail — it identifies the behavioral and operational signals that precede failure and surfaces them while there is still time to recover.
The Failure Prediction Model
The model is built on three input categories: behavioral signals (how recruiters and hiring managers are acting), pipeline signals (how candidates are moving through the funnel), and historical correlation (what patterns have preceded failure on comparable mandates).
Behavioral Signal Monitoring
The system tracks recruiter activity patterns, hiring manager response latency, and feedback quality. A recruiter managing 6 active mandates who takes 48+ hours to respond to candidate replies is exhibiting a load signature that correlates with mandate failure within 3 weeks.
Pipeline Pattern Recognition
Certain pipeline shapes predict failure before any individual metric looks alarming. A top-of-funnel that is generating volume but converting poorly to shortlist is a different failure type than a mandate where sourcing has stopped entirely. The model distinguishes between them and triggers different responses.
Historical Correlation
Every mandate Majhi OS manages contributes to the prediction model. When a current search matches the behavioral and pipeline signature of mandates that failed in the past — same role type, similar market conditions, comparable recruiter load — the system surfaces the pattern. The model compounds over time: more mandates means more accurate predictions.
Threshold Breach Detection
Specific thresholds trigger immediate escalation regardless of the broader model. Response decay exceeding 40% over 14 days. Pipeline velocity dropping below 50% of role-type benchmark. Recruiter load crossing the capacity threshold. These are hard triggers — they don't require model confirmation to fire.
What Gets Flagged
Outreach Decay
Reply rates declining faster than baseline for this role type and market segment. Often the first visible signal of a mandate losing momentum.
Pipeline Compression
Fewer candidates are advancing between stages than comparable mandates at the same point in the search. The funnel is narrowing prematurely.
Recruiter Overload
The assigned recruiter's load has exceeded their capacity threshold. Quality of outreach and follow-up degrades under load before any metric shows it explicitly.
Intake Misalignment
Shortlist approval rate below 38% indicates a mismatch between the candidate profile being sourced and what the hiring manager actually wants. Without correction, the pipeline fills with the wrong people.
Engagement Collapse
Candidates who were engaged are going silent. Often caused by a slow interview process — if competitors are moving faster, your best candidates exit quietly.
Velocity Stall
The search has been open longer than the predicted close date without an explanation. Time on mandate is a leading indicator of cost and eventual failure.
From Detection to Action
Detection without response is just a dashboard. When the Failure Prediction Engine flags a mandate, it doesn't just alert a recruiter — it identifies the specific failure type and selects the appropriate Recovery Playbook. The action follows automatically from the diagnosis.
"The difference between a stalled search and a recovered search is usually a 72-hour window. Most teams don't know they're in that window until it has already passed."
Why This Can't Be Done Manually
A recruiting team managing 6–8 active mandates simultaneously doesn't have the bandwidth to monitor 12 signals per mandate in real time. They prioritize based on which hiring managers are loudest, not which mandates are closest to failure. The Failure Prediction Engine removes the dependency on recruiter attention — it monitors everything continuously and surfaces what needs action, when it needs it.